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Motion planning and feedback control techniques with applications to long tractor-trailer vehicles
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1795-5992
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. At the same time, there has been a growing demand within the transportation sector to increase efficiency and to reduce the environmental impact related to transportation of people and goods. Therefore, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems and self-driving vehicles.

Autonomous vehicles are expected to have their first big impact in closed environments, such as mines, harbors, loading and offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, tractor-trailer vehicles are frequently used for transportation. These vehicles are composed of several interconnected vehicle segments, and are therefore large, complex and unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control techniques for such systems.

The contributions of this thesis are within the area of motion planning and feedback control for long tractor-trailer combinations operating at low-speeds in closed and unstructured environments. It includes development of motion planning and feedback control frameworks, structured design tools for guaranteeing closed-loop stability and experimental validation of the proposed solutions through simulations, lab and field experiments. Even though the primary application in this work is tractor-trailer vehicles, many of the proposed approaches can with some adjustments also be used for other systems, such as drones and ships.

The developed sampling-based motion planning algorithms are based upon the probabilistic closed-loop rapidly exploring random tree (CL-RRT) algorithm and the deterministic lattice-based motion planning algorithm. It is also proposed to use numerical optimal control offline for precomputing libraries of optimized maneuvers as well as during online planning in the form of a warm-started optimization step.

To follow the motion plan, several predictive path-following control approaches are proposed with different computational complexity and performance. Common for these approaches are that they use a path-following error model of the vehicle for future predictions and are tailored to operate in series with a motion planner that computes feasible paths. The design strategies for the path-following approaches include linear quadratic (LQ) control and several advanced model predictive control (MPC) techniques to account for physical and sensing limitations. To strengthen the practical value of the developed techniques, several of the proposed approaches have been implemented and successfully demonstrated in field experiments on a full-scale test platform. To estimate the vehicle states needed for control, a novel nonlinear observer is evaluated on the full-scale test vehicle. It is designed to only utilize information from sensors that are mounted on the tractor, making the system independent of any sensor mounted on the trailer.

Abstract [sv]

Under de senaste årtiondena har utvecklingen av sensor- och hårdvaruteknik gått i en snabb takt, samtidigt som nya metoder och algoritmer har introducerats. Samtidigt ställs det stora krav på transportsektorn att öka effektiviteten och minska miljöpåverkan vid transporter av både människor och varor. Som en följd av detta har många ledande fordonstillverkare och teknikföretag börjat satsat på att utveckla avancerade förarstödsystem och självkörande fordon. Även forskningen inom autonoma fordon har under de senaste årtiondena kraftig ökat då en rad tekniska problem återstår att lösas.

Förarlösa fordon förväntas få sitt första stora genombrott i slutna miljöer, såsom gruvor, hamnar, lastnings- och lossningsplatser. I sådana områden är lagstiftningen mindre hård jämfört med stadsområden och omgivningen är mer kontrollerad och förutsägbar. Några av de förväntade positiva effekterna är ökad produktivitet och säkerhet, minskade utsläpp och möjligheten att avlasta människor från att utföra svåra eller farliga uppgifter. Inom dessa platser används ofta lastbilar med olika släpvagnskombinationer för att transportera material. En sådan fordonskombination är uppbyggd av flera ihopkopplade moduler och är således utmanande att backa då systemet är instabilt. Detta gör det svårt att utforma ramverk för att styra sådana system vid exempelvis autonom backning.

Självkörande fordon är mycket komplexa system som består av en rad olika komponenter vilka är designade för att lösa separata delproblem. Två viktiga komponenter i ett självkörande fordon är dels rörelseplaneraren som har i uppgift att planera hur fordonet ska röra sig för att på ett säkert sätt nå ett överordnat mål, och dels den banföljande regulatorn vars uppgift är att se till att den planerade manövern faktiskt utförs i praktiken trots störningar och modellfel. I denna avhandling presenteras flera olika algoritmer för att planera och utföra komplexa manövrar för lastbilar med olika typer av släpvagnskombinationer. De presenterade algoritmerna är avsedda att användas som avancerade förarstödsystem eller som komponenter i ett helt autonomt system. Även om den primära applikationen i denna avhandling är lastbilar med släp, kan många av de förslagna algoritmerna även användas för en rad andra system, så som drönare och båtar.

Experimentell validering är viktigt för att motivera att en föreslagen algoritm är användbar i praktiken. I denna avhandling har flera av de föreslagna planerings- och reglerstrategierna implementerats på en småskalig testplattform och utvärderats i en kontrollerad labbmiljö. Utöver detta har även flera av de föreslagna ramverken implementerats och utvärderats i fältexperiment på en fullskalig test-plattform som har utvecklats i samarbete med Scania CV. Här utvärderas även en ny metod för att skatta släpvagnens beteende genom att endast utnyttja information från sensorer monterade på lastbilen, vilket gör det föreslagna ramverket oberoende av sensorer monterade på släpvagnen.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. , p. 89
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2070
National Category
Control Engineering Robotics
Identifiers
URN: urn:nbn:se:liu:diva-165246DOI: 10.3384/diss.diva-165246ISBN: 9789179298586 (print)OAI: oai:DiVA.org:liu-165246DiVA, id: diva2:1424832
Public defence
2020-05-29, Ada Lovelace, B-building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
Vinnova, 2017-01957Available from: 2020-04-20 Created: 2020-04-20 Last updated: 2020-04-27Bibliographically approved
List of papers
1. Motion planning for a reversing general 2-trailer configuration using Closed-Loop RRT
Open this publication in new window or tab >>Motion planning for a reversing general 2-trailer configuration using Closed-Loop RRT
2016 (English)In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3690-3697Conference paper, Published paper (Refereed)
Abstract [en]

Reversing with a dolly steered trailer configura- tion is a hard task for any driver without extensive training. In this work we present a motion planning and control framework that can be used to automatically plan and execute complicated manoeuvres. The unstable dynamics of the reversing general 2- trailer configuration with off-axle hitching is first stabilised by an LQ-controller and then a pure pursuit path tracker is used on a higher level giving a cascaded controller that can track piecewise linear reference paths. This controller together with a kinematic model of the trailer configuration is then used for forward simulations within a Closed-Loop Rapidly Exploring Random Tree framework to generate motion plans that are not only kinematically feasible but also include the limitations of the controller’s tracking performance when reversing. The approach is evaluated over a series of Monte Carlo simulations on three different scenarios and impressive success rates are achieved. Finally the approach is successfully tested on a small scale test platform where the motion plan is calculated and then sent to the platform for execution. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
Intelligent Robots and Systems, E-ISSN 2153-0866 ; 2016
National Category
Robotics Control Engineering
Identifiers
urn:nbn:se:liu:diva-134035 (URN)10.1109/IROS.2016.7759544 (DOI)000391921703107 ()9781509037629 (ISBN)9781509037612 (ISBN)9781509037636 (ISBN)
Conference
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, October 9-14, 2016
Projects
iQMatic
Note

Funding agencies: FFI/VINNOVA

Available from: 2017-01-19 Created: 2017-01-19 Last updated: 2020-04-27Bibliographically approved
2. Path following control for a reversing general 2-trailer system
Open this publication in new window or tab >>Path following control for a reversing general 2-trailer system
2016 (English)In: 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2016, p. 2455-2461Conference paper, Published paper (Refereed)
Abstract [en]

In order to meet the requirements for autonomous systems in real world applications, reliable path following controllers have to be designed to execute planned paths despite the existence of disturbances and model errors. In this paper we propose a Linear Quadratic controller for stabilizing a 2-trailer system with possible off-axle hitching around preplanned paths in backward motion. The controller design is based on a kinematic model of a general 2-trailer system including the possibility for off-axle hitching. Closed-loop stability is proved around a set of paths, typically chosen to represent the possible output from the path planner, using theory from linear differential inclusions. Using convex optimization tools a single quadratic Lyapunov function is computed for the entire set of paths.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-138327 (URN)10.1109/CDC.2016.7798630 (DOI)000400048102102 ()978-1-5090-1837-6 (ISBN)
Conference
55th IEEE Conference on Decision and Control (CDC)
Available from: 2017-06-13 Created: 2017-06-13 Last updated: 2020-04-27
3. On stability for state-lattice trajectory tracking control
Open this publication in new window or tab >>On stability for state-lattice trajectory tracking control
2018 (English)In: 2018 Annual American Control Conference (ACC), IEEE, 2018, p. 5868-5875Conference paper, Published paper (Refereed)
Abstract [en]

In order to guarantee that a self-driving vehicle is behaving as expected, stability of the closed-loop system needs to be rigorously analyzed. The key components for the lowest levels of control in self-driving vehicles are the controlled vehicle, the low-level controller and the local planner.The local planner that is considered in this work constructs a feasible trajectory by combining a finite number of precomputed motions. When this local planner is considered, we show that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, we propose a novel method for analyzing the behavior of the tracking error, how to design the low-level controller and how to potentially impose constraints on the local planner, in order to guarantee that the tracking error is bounded and decays towards zero. The proposed method is applied on a truck and trailer system and the results are illustrated in two simulation examples.

Place, publisher, year, edition, pages
IEEE, 2018
Series
American Control Conference (ACC), E-ISSN 2378-5861
National Category
Control Engineering Robotics
Identifiers
urn:nbn:se:liu:diva-152455 (URN)10.23919/ACC.2018.8430822 (DOI)978-1-5386-5428-6 (ISBN)978-1-5386-5427-9 (ISBN)978-1-5386-5429-3 (ISBN)
Conference
2018 Annual American Control Conference (ACC) June 27–29, 2018. Wisconsin Center, Milwaukee, USA
Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2020-04-27
4. A path planning and path-following control framework for a general 2-trailer with a car-like tractor
Open this publication in new window or tab >>A path planning and path-following control framework for a general 2-trailer with a car-like tractor
Show others...
2019 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967Article in journal (Refereed) Epub ahead of print
Abstract [en]

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires a significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed, which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path-planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.

Place, publisher, year, edition, pages
WILEY, 2019
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-161845 (URN)10.1002/rob.21908 (DOI)000492389900001 ()
Note

Funding Agencies|Strategic vehicle research and innovation programme (FFI); Scania CV [2017-01957]

Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2020-04-27

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